SBIR-STTR Award

A new family of what is known as Web 2.0 applications is currently emerging on the Web as means to create and share web resources. These applications include user-centric publishing and knowledge management platforms like Wikis and Blogs. Social resource sharing systems have acquired several million users in just few years. As the use of folksonomy proliferate users feel the need for more structure for better organizing their resources and enhance resource discovery and retrieval. We believe that exposing such latent structure will enhance user experience in a wide range of applications including situation awareness. We conjecture that a complete solution that is able to deduce latent structures in user vocabulary must account for controlled and uncontrolled vocabularies as well as the three elements of the social tagging triangle: people, tags, and resources. Controlled vocabularies could be as limited as domain ontology but it could also be as wide as Wordnet, Propbank or Framenet. In our view, a successful system design an implementation should not restrict to a specific vocabularies. Instead we propose to develop general solutions that extract latent structures in folksonomies through mapping to semantically richer controlled vocabularies.

Keywords:
Folksonomy Latent_semantics Web2.0 Controlled_vocabularies Semantics

Managing millions of friends and followers on social sites is increasingly challenging, time consuming, expensive and complicated for a growing number of companies around the world. Effective tools and resources are needed to manage and leverage mission-critical information on people, content and activities in their social networks, blogs, and other social media outlets. the main problem to be tackled in this research is to answer the question “given user personomies and community folksonomies, what are their preferences and interests?� By extracting the information latent in personomies and folksonomies, our goal is to infer the unique insights about users and communities in collaborative tagging sites. Our proposed effort fundamentally differs from other related research in that our focus is on individuals, while others focus on items and content alone. Resolving the complexities described in this proposal will enable our technology to infer people’s connections by monitoring behavior, accurately computing sentiment, uncovering previously inaccessible facets of user persona, and matching Persona Fingerprints across social sites to determine aliases.

Keywords:
Folksonomy, Personomy, Semantic, Tagging, Behavior, Persona, Latent Information